%0 Journal Article
%T Comparison of Electric Vehicle¡¯s Energy Consumption Factors for Different Road Types
%A Enjian Yao
%A Zhiqiang Yang
%A Yuanyuan Song
%A Ting Zuo
%J Discrete Dynamics in Nature and Society
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/328757
%X Energy-optimal route planning for electric vehicle (EV) is highly required for the wide-spread use of EV, which is hindered by limited battery capacity and relative short cruising range. Obtaining the cost for each link (i.e., link energy consumption) in road networks plays a key role in energy-optimal route planning process. The link energy consumption depends mainly on energy consumption factor, which is related to not only vehicle speed but also road type. This study aims to analyze the difference of EV¡¯s energy consumption factors for different road types. According to the floating car data (FCD) collected from the road network in Beijing, the vehicle specific power (VSP) distributions under different average travel speeds for different road types are analyzed firstly, and then the EV¡¯s energy consumption rates under different VSP-Bins are calculated. By using VSP as an intermediate variable, EV¡¯s energy consumption factor models for different road types are established and the difference of EV¡¯s energy consumption factors is analyzed. The results show that road type-based energy consumption factor should be used in EV¡¯s energy-optimal route planning process. 1. Introduction Recently, to cope with the problem of pollutant emissions and energy consumption caused by gasoline and diesel powered vehicles, increasing attention has been paid to EVs due to the advantages of zero-emission during use, low noise, and high energy efficiency. However, the relative short cruising range has become one of the main obstacles to the development and wide-spread use of EVs [1]. Some intelligent transportation systems (ITS) solutions such as energy-efficient route planning and charging facilities guidance systems are proposed to help EV drivers to optimize travel route and find charging station timely, which are expected to alleviate the restrictions resulted from the relative short cruising range of EVs. Estimating EV¡¯s energy consumption accurately is a prerequisite in route planning and navigation systems [2¨C4]; therefore it is of great significance to estimate EV¡¯s energy consumption. Many researches on EV¡¯s energy consumption estimation are based on ideal running status, whose results cannot reflect the effect of the actual vehicle running status. Physicallybased methods, which take into account vehicle driving parameters (i.e., average travel speed), are usually used in modeling fuel consumption of gasoline and diesel powered vehicles [5]. In these models, VSP is introduced as an intermediate variable for the ability to build the relationship between energy
%U http://www.hindawi.com/journals/ddns/2013/328757/